S7 | py-shiny | |
---|---|---|
6 | 31 | |
440 | 1,422 | |
2.9% | 4.2% | |
9.3 | 9.5 | |
4 months ago | 6 days ago | |
R | Python | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
S7
- Will they get it right this time?
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Tidyverse 2.0.0
https://adv-r.hadley.nz/oo.html
"There are multiple OOP systems to choose from. In this book, I’ll focus on the three that I believe are most important: S3, R6, and S4. S3 and S4 are provided by base R. R6 is provided by the R6 package, and is similar to the Reference Classes, or RC for short, from base R.
"There is disagreement about the relative importance of the OOP systems. I think S3 is most important, followed by R6, then S4. Others believe that S4 is most important, followed by RC, and that S3 should be avoided. This means that different R communities use different systems."
https://rconsortium.github.io/OOP-WG/
"The S7 package is a new OOP system designed to be a successor to S3 and S4."
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Is python necessary to learn machine learning?
Even if RStudio & the Tidyverse have mostly been promoting a functional programming style in R, it has full support for OOP (see R6 or R7 for more modern implementations of it). Let's not even mention the excellent Stan ecosystem for Probabilistic programming / Bayesian modeling, or Bioconductor, the biggest repository of bioinformatics packages & tools of any language.
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Why is OOP in R so messy?
Not sure if you or others have missed it, as the link from the readme is dead, but the proposal section of that repo is informative of the current state of things: https://github.com/RConsortium/OOP-WG/blob/master/proposal/proposal.org
py-shiny
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Reactive Signals for Python – inspired by Angular's reactivity model
Shiny from the above commenter is a good example of the dirty / “invalidate” style of algorithm.
https://github.com/posit-dev/py-shiny/blob/main/shiny/reacti...
https://github.com/posit-dev/py-shiny/blob/main/shiny/reacti...
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Designing a Pure Python Web Framework
I really like this idea of using Python to create both the frontend and backend. Another lib doing this is https://solara.dev/ . Something I particularly like about Solara is that you can interactively build your app in a Jupyter Notebook, since behind the scenes it's using ipywidgets.
Has anyone compared Solara and Reflex and can comment on pros/cons? Are there other options in this space? Maybe https://shiny.posit.co/py/ ?
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FastUI: Build Better UIs Faster
Would you consider giving Shiny (for Python) a try? https://shiny.posit.co/py/ It's (I hope) pretty close to Streamlit in ease of use for getting started, but reactive programming runs all the way through it. The kind of app you're talking about are extremely natural to write in Shiny, you don't have to keep track of state yourself at all.
If you decide to give it a try and have trouble, please email me (email in profile) or drop by the Discord (https://discord.gg/yMGCamUMnS).
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py-shiny VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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Duckdb + Shiny for Python example
Code is here: https://github.com/rstudio/py-shiny/tree/duckdb-example/examples/duckdb
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Transitioning from R to Python - any tips?
The equivalent of shiny in python is shiny for python: https://shiny.posit.co/py/
- Show HN: Mercury – convert Jupyter Notebooks to Web Apps without code rewriting
- Shiny for Python – building interactive web apps from Python
- Shiny – Web Pages in Python
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Tidyverse 2.0.0
I'm not sure how usable it is, but Shiny for Python exists: https://shiny.rstudio.com/py/
What are some alternatives?
AlgebraOfGraphics.jl - An algebraic spin on grammar-of-graphics data visualization in Julia. Powered by the Makie.jl plotting ecosystem.
Solara - A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps
stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
pyvibe - Generate styled HTML pages from Python
dtplyr - Data table backend for dplyr
deptry - Find unused, missing and transitive dependencies in a Python project.